Overview

Dataset statistics

Number of variables13
Number of observations2965
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory301.3 KiB
Average record size in memory104.0 B

Variable types

Numeric13

Alerts

gross_revenue is highly correlated with qtde_invoices and 4 other fieldsHigh correlation
recency_days is highly correlated with qtde_invoicesHigh correlation
qtde_invoices is highly correlated with gross_revenue and 3 other fieldsHigh correlation
qtde_items is highly correlated with gross_revenue and 4 other fieldsHigh correlation
qtde_products is highly correlated with gross_revenue and 2 other fieldsHigh correlation
avg_recency_days is highly correlated with frequencyHigh correlation
frequency is highly correlated with avg_recency_daysHigh correlation
avg_basket_size is highly correlated with gross_revenue and 2 other fieldsHigh correlation
avg_unique_basket_size is highly correlated with gross_revenue and 2 other fieldsHigh correlation
gross_revenue is highly correlated with qtde_invoices and 1 other fieldsHigh correlation
qtde_invoices is highly correlated with gross_revenue and 2 other fieldsHigh correlation
qtde_items is highly correlated with gross_revenue and 1 other fieldsHigh correlation
qtde_products is highly correlated with qtde_invoicesHigh correlation
avg_ticket is highly correlated with qtde_returns and 2 other fieldsHigh correlation
qtde_returns is highly correlated with avg_ticketHigh correlation
avg_basket_size is highly correlated with avg_ticket and 1 other fieldsHigh correlation
avg_unique_basket_size is highly correlated with avg_ticket and 1 other fieldsHigh correlation
gross_revenue is highly correlated with qtde_invoices and 2 other fieldsHigh correlation
qtde_invoices is highly correlated with gross_revenue and 2 other fieldsHigh correlation
qtde_items is highly correlated with gross_revenue and 4 other fieldsHigh correlation
qtde_products is highly correlated with gross_revenue and 2 other fieldsHigh correlation
avg_recency_days is highly correlated with frequencyHigh correlation
frequency is highly correlated with avg_recency_daysHigh correlation
avg_basket_size is highly correlated with qtde_items and 1 other fieldsHigh correlation
avg_unique_basket_size is highly correlated with qtde_items and 1 other fieldsHigh correlation
gross_revenue is highly correlated with qtde_invoices and 5 other fieldsHigh correlation
qtde_invoices is highly correlated with gross_revenue and 3 other fieldsHigh correlation
qtde_items is highly correlated with gross_revenue and 5 other fieldsHigh correlation
qtde_products is highly correlated with gross_revenue and 3 other fieldsHigh correlation
avg_ticket is highly correlated with qtde_returns and 2 other fieldsHigh correlation
qtde_returns is highly correlated with gross_revenue and 6 other fieldsHigh correlation
avg_basket_size is highly correlated with gross_revenue and 4 other fieldsHigh correlation
avg_unique_basket_size is highly correlated with gross_revenue and 4 other fieldsHigh correlation
avg_ticket is highly skewed (γ1 = 25.14445126) Skewed
frequency is highly skewed (γ1 = 24.92957584) Skewed
qtde_returns is highly skewed (γ1 = 21.97395499) Skewed
df_index has unique values Unique
customer_id has unique values Unique
recency_days has 33 (1.1%) zeros Zeros
qtde_returns has 1480 (49.9%) zeros Zeros

Reproduction

Analysis started2021-11-19 18:49:33.261086
Analysis finished2021-11-19 18:49:55.940572
Duration22.68 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

UNIQUE

Distinct2965
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2308.542664
Minimum0
Maximum5689
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2021-11-19T15:49:56.029574image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile185.2
Q1926
median2113
Q33523
95-th percentile5010.2
Maximum5689
Range5689
Interquartile range (IQR)2597

Descriptive statistics

Standard deviation1547.283204
Coefficient of variation (CV)0.670242412
Kurtosis-1.011794798
Mean2308.542664
Median Absolute Deviation (MAD)1265
Skewness0.3400689729
Sum6844829
Variance2394085.312
MonotonicityStrictly increasing
2021-11-19T15:49:56.147720image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
< 0.1%
30021
 
< 0.1%
29871
 
< 0.1%
29901
 
< 0.1%
29911
 
< 0.1%
29921
 
< 0.1%
29931
 
< 0.1%
29961
 
< 0.1%
29981
 
< 0.1%
29991
 
< 0.1%
Other values (2955)2955
99.7%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
ValueCountFrequency (%)
56891
< 0.1%
56701
< 0.1%
56601
< 0.1%
56541
< 0.1%
56331
< 0.1%
56291
< 0.1%
56231
< 0.1%
56121
< 0.1%
56111
< 0.1%
56011
< 0.1%

customer_id
Real number (ℝ≥0)

UNIQUE

Distinct2965
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15270.24992
Minimum12347
Maximum18287
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2021-11-19T15:49:56.274955image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum12347
5-th percentile12619.2
Q113799
median15220
Q316770
95-th percentile17964.8
Maximum18287
Range5940
Interquartile range (IQR)2971

Descriptive statistics

Standard deviation1719.522705
Coefficient of variation (CV)0.1126060617
Kurtosis-1.206368645
Mean15270.24992
Median Absolute Deviation (MAD)1489
Skewness0.03249797116
Sum45276291
Variance2956758.332
MonotonicityNot monotonic
2021-11-19T15:49:56.394335image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
178501
 
< 0.1%
175881
 
< 0.1%
149051
 
< 0.1%
161031
 
< 0.1%
146261
 
< 0.1%
148681
 
< 0.1%
182461
 
< 0.1%
171151
 
< 0.1%
166111
 
< 0.1%
159121
 
< 0.1%
Other values (2955)2955
99.7%
ValueCountFrequency (%)
123471
< 0.1%
123481
< 0.1%
123521
< 0.1%
123561
< 0.1%
123581
< 0.1%
123591
< 0.1%
123601
< 0.1%
123621
< 0.1%
123641
< 0.1%
123701
< 0.1%
ValueCountFrequency (%)
182871
< 0.1%
182831
< 0.1%
182821
< 0.1%
182771
< 0.1%
182761
< 0.1%
182741
< 0.1%
182731
< 0.1%
182721
< 0.1%
182701
< 0.1%
182691
< 0.1%

gross_revenue
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2950
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2693.517008
Minimum6.2
Maximum279138.02
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2021-11-19T15:49:56.519013image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum6.2
5-th percentile230.892
Q1570.96
median1084.1
Q32308.06
95-th percentile7180.164
Maximum279138.02
Range279131.82
Interquartile range (IQR)1737.1

Descriptive statistics

Standard deviation10118.81467
Coefficient of variation (CV)3.75672945
Kurtosis398.8753032
Mean2693.517008
Median Absolute Deviation (MAD)671.32
Skewness17.65668291
Sum7986277.93
Variance102390410.3
MonotonicityNot monotonic
2021-11-19T15:49:56.635971image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2053.022
 
0.1%
1353.742
 
0.1%
734.942
 
0.1%
1025.442
 
0.1%
598.22
 
0.1%
533.332
 
0.1%
731.92
 
0.1%
2092.322
 
0.1%
379.652
 
0.1%
745.062
 
0.1%
Other values (2940)2945
99.3%
ValueCountFrequency (%)
6.21
< 0.1%
13.31
< 0.1%
36.561
< 0.1%
451
< 0.1%
521
< 0.1%
52.21
< 0.1%
52.21
< 0.1%
62.431
< 0.1%
68.841
< 0.1%
70.021
< 0.1%
ValueCountFrequency (%)
279138.021
< 0.1%
259657.31
< 0.1%
194550.791
< 0.1%
136263.721
< 0.1%
124564.531
< 0.1%
116725.631
< 0.1%
91062.381
< 0.1%
72882.091
< 0.1%
66653.561
< 0.1%
65019.621
< 0.1%

recency_days
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct272
Distinct (%)9.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.21551433
Minimum0
Maximum373
Zeros33
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2021-11-19T15:49:56.758935image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q111
median31
Q381
95-th percentile242
Maximum373
Range373
Interquartile range (IQR)70

Descriptive statistics

Standard deviation77.5781772
Coefficient of variation (CV)1.20809088
Kurtosis2.760313031
Mean64.21551433
Median Absolute Deviation (MAD)26
Skewness1.79469707
Sum190399
Variance6018.373578
MonotonicityNot monotonic
2021-11-19T15:49:56.881118image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
199
 
3.3%
487
 
2.9%
385
 
2.9%
284
 
2.8%
876
 
2.6%
1067
 
2.3%
766
 
2.2%
966
 
2.2%
1764
 
2.2%
2255
 
1.9%
Other values (262)2216
74.7%
ValueCountFrequency (%)
033
 
1.1%
199
3.3%
284
2.8%
385
2.9%
487
2.9%
543
1.5%
766
2.2%
876
2.6%
966
2.2%
1067
2.3%
ValueCountFrequency (%)
3732
0.1%
3723
0.1%
3711
 
< 0.1%
3681
 
< 0.1%
3664
0.1%
3652
0.1%
3641
 
< 0.1%
3601
 
< 0.1%
3591
 
< 0.1%
3584
0.1%

qtde_invoices
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct57
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.724114671
Minimum1
Maximum206
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2021-11-19T15:49:57.025800image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile17
Maximum206
Range205
Interquartile range (IQR)4

Descriptive statistics

Standard deviation8.847148679
Coefficient of variation (CV)1.545592495
Kurtosis190.1694352
Mean5.724114671
Median Absolute Deviation (MAD)2
Skewness10.7455824
Sum16972
Variance78.27203974
MonotonicityNot monotonic
2021-11-19T15:49:57.153669image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2785
26.5%
3496
16.7%
4394
13.3%
5236
 
8.0%
1189
 
6.4%
6173
 
5.8%
7139
 
4.7%
898
 
3.3%
969
 
2.3%
1054
 
1.8%
Other values (47)332
11.2%
ValueCountFrequency (%)
1189
 
6.4%
2785
26.5%
3496
16.7%
4394
13.3%
5236
 
8.0%
6173
 
5.8%
7139
 
4.7%
898
 
3.3%
969
 
2.3%
1054
 
1.8%
ValueCountFrequency (%)
2061
< 0.1%
1981
< 0.1%
1241
< 0.1%
971
< 0.1%
911
< 0.1%
901
< 0.1%
861
< 0.1%
721
< 0.1%
622
0.1%
601
< 0.1%

qtde_items
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1670
Distinct (%)56.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1581.048229
Minimum2
Maximum196844
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2021-11-19T15:49:57.274904image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile103
Q1297
median641
Q31399
95-th percentile4404
Maximum196844
Range196842
Interquartile range (IQR)1102

Descriptive statistics

Standard deviation5702.809377
Coefficient of variation (CV)3.606980022
Kurtosis517.7456485
Mean1581.048229
Median Absolute Deviation (MAD)422
Skewness18.75302157
Sum4687808
Variance32522034.79
MonotonicityNot monotonic
2021-11-19T15:49:57.400106image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31011
 
0.4%
889
 
0.3%
1509
 
0.3%
848
 
0.3%
2468
 
0.3%
2608
 
0.3%
1348
 
0.3%
2728
 
0.3%
2888
 
0.3%
12007
 
0.2%
Other values (1660)2881
97.2%
ValueCountFrequency (%)
22
0.1%
122
0.1%
161
< 0.1%
171
< 0.1%
181
< 0.1%
191
< 0.1%
201
< 0.1%
231
< 0.1%
251
< 0.1%
261
< 0.1%
ValueCountFrequency (%)
1968441
< 0.1%
798791
< 0.1%
773731
< 0.1%
699931
< 0.1%
645491
< 0.1%
641241
< 0.1%
628121
< 0.1%
582431
< 0.1%
577721
< 0.1%
502551
< 0.1%

qtde_products
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct468
Distinct (%)15.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean122.791231
Minimum1
Maximum7837
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2021-11-19T15:49:57.528162image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9
Q129
median67
Q3135
95-th percentile382
Maximum7837
Range7836
Interquartile range (IQR)106

Descriptive statistics

Standard deviation269.3926153
Coefficient of variation (CV)2.193907603
Kurtosis354.0870004
Mean122.791231
Median Absolute Deviation (MAD)44
Skewness15.67183245
Sum364076
Variance72572.38116
MonotonicityNot monotonic
2021-11-19T15:49:57.651037image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2846
 
1.6%
2038
 
1.3%
3535
 
1.2%
1533
 
1.1%
2932
 
1.1%
1132
 
1.1%
1932
 
1.1%
2530
 
1.0%
2630
 
1.0%
2730
 
1.0%
Other values (458)2627
88.6%
ValueCountFrequency (%)
15
 
0.2%
214
0.5%
315
0.5%
417
0.6%
526
0.9%
628
0.9%
718
0.6%
819
0.6%
927
0.9%
1027
0.9%
ValueCountFrequency (%)
78371
< 0.1%
55861
< 0.1%
50951
< 0.1%
45771
< 0.1%
26971
< 0.1%
23791
< 0.1%
20601
< 0.1%
18181
< 0.1%
16721
< 0.1%
16361
< 0.1%

avg_ticket
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct2963
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.00896375
Minimum2.150588235
Maximum4453.43
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2021-11-19T15:49:57.780849image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum2.150588235
5-th percentile4.913568645
Q113.11933333
median17.94344444
Q324.97962963
95-th percentile90.2355
Maximum4453.43
Range4451.279412
Interquartile range (IQR)11.8602963

Descriptive statistics

Standard deviation119.5916637
Coefficient of variation (CV)3.623005697
Kurtosis812.1529304
Mean33.00896375
Median Absolute Deviation (MAD)5.974748792
Skewness25.14445126
Sum97871.57751
Variance14302.16603
MonotonicityNot monotonic
2021-11-19T15:49:57.897834image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14.478333332
 
0.1%
4.1622
 
0.1%
18.152222221
 
< 0.1%
21.474358971
 
< 0.1%
3.4119452891
 
< 0.1%
16.293720931
 
< 0.1%
36.244117651
 
< 0.1%
29.784166671
 
< 0.1%
22.87926231
 
< 0.1%
20.511041671
 
< 0.1%
Other values (2953)2953
99.6%
ValueCountFrequency (%)
2.1505882351
< 0.1%
2.43251
< 0.1%
2.4623711341
< 0.1%
2.5112413791
< 0.1%
2.5153333331
< 0.1%
2.651
< 0.1%
2.6569318181
< 0.1%
2.7075982531
< 0.1%
2.7606215721
< 0.1%
2.7704641911
< 0.1%
ValueCountFrequency (%)
4453.431
< 0.1%
3202.921
< 0.1%
1687.21
< 0.1%
952.98751
< 0.1%
872.131
< 0.1%
841.02144931
< 0.1%
651.16833331
< 0.1%
6401
< 0.1%
624.41
< 0.1%
615.751
< 0.1%

avg_recency_days
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct1258
Distinct (%)42.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67.33544331
Minimum1
Maximum366
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2021-11-19T15:49:58.026512image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q126
median48.28571429
Q385.33333333
95-th percentile200.8
Maximum366
Range365
Interquartile range (IQR)59.33333333

Descriptive statistics

Standard deviation63.5194806
Coefficient of variation (CV)0.9433290623
Kurtosis4.904182516
Mean67.33544331
Median Absolute Deviation (MAD)26.28571429
Skewness2.065743134
Sum199649.5894
Variance4034.724415
MonotonicityNot monotonic
2021-11-19T15:49:58.149509image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1425
 
0.8%
421
 
0.7%
7021
 
0.7%
720
 
0.7%
3518
 
0.6%
4918
 
0.6%
4617
 
0.6%
1117
 
0.6%
2117
 
0.6%
2616
 
0.5%
Other values (1248)2775
93.6%
ValueCountFrequency (%)
116
0.5%
1.51
 
< 0.1%
213
0.4%
2.51
 
< 0.1%
2.6013986011
 
< 0.1%
315
0.5%
3.3214285711
 
< 0.1%
3.3303571431
 
< 0.1%
3.52
 
0.1%
421
0.7%
ValueCountFrequency (%)
3661
 
< 0.1%
3651
 
< 0.1%
3631
 
< 0.1%
3621
 
< 0.1%
3572
0.1%
3561
 
< 0.1%
3552
0.1%
3521
 
< 0.1%
3512
0.1%
3503
0.1%

frequency
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct1223
Distinct (%)41.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1134496828
Minimum0.005449591281
Maximum17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2021-11-19T15:49:58.274920image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.005449591281
5-th percentile0.008891526541
Q10.01633986928
median0.02588996764
Q30.04929577465
95-th percentile1
Maximum17
Range16.99455041
Interquartile range (IQR)0.03295590537

Descriptive statistics

Standard deviation0.408066171
Coefficient of variation (CV)3.596891246
Kurtosis991.6621992
Mean0.1134496828
Median Absolute Deviation (MAD)0.0121913375
Skewness24.92957584
Sum336.3783096
Variance0.1665179999
MonotonicityNot monotonic
2021-11-19T15:49:58.398852image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1197
 
6.6%
0.062517
 
0.6%
0.0277777777817
 
0.6%
0.0238095238116
 
0.5%
0.0909090909115
 
0.5%
0.0833333333315
 
0.5%
0.0344827586214
 
0.5%
0.0294117647114
 
0.5%
0.0769230769213
 
0.4%
0.0256410256413
 
0.4%
Other values (1213)2634
88.8%
ValueCountFrequency (%)
0.0054495912811
 
< 0.1%
0.0054644808741
 
< 0.1%
0.0054794520551
 
< 0.1%
0.0054945054951
 
< 0.1%
0.0055865921792
0.1%
0.0056022408961
 
< 0.1%
0.0056179775282
0.1%
0.005665722381
 
< 0.1%
0.0056818181822
0.1%
0.0056980056983
0.1%
ValueCountFrequency (%)
171
 
< 0.1%
31
 
< 0.1%
26
 
0.2%
1.1428571431
 
< 0.1%
1197
6.6%
0.751
 
< 0.1%
0.66666666673
 
0.1%
0.5508021391
 
< 0.1%
0.53083109921
 
< 0.1%
0.53
 
0.1%

qtde_returns
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct213
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.82765599
Minimum0
Maximum9014
Zeros1480
Zeros (%)49.9%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2021-11-19T15:49:58.538928image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q39
95-th percentile99.6
Maximum9014
Range9014
Interquartile range (IQR)9

Descriptive statistics

Standard deviation282.96619
Coefficient of variation (CV)8.124755514
Kurtosis595.9615111
Mean34.82765599
Median Absolute Deviation (MAD)1
Skewness21.97395499
Sum103264
Variance80069.86469
MonotonicityNot monotonic
2021-11-19T15:49:58.659055image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01480
49.9%
1164
 
5.5%
2147
 
5.0%
3105
 
3.5%
489
 
3.0%
678
 
2.6%
561
 
2.1%
1251
 
1.7%
743
 
1.5%
843
 
1.5%
Other values (203)704
23.7%
ValueCountFrequency (%)
01480
49.9%
1164
 
5.5%
2147
 
5.0%
3105
 
3.5%
489
 
3.0%
561
 
2.1%
678
 
2.6%
743
 
1.5%
843
 
1.5%
941
 
1.4%
ValueCountFrequency (%)
90141
< 0.1%
80041
< 0.1%
44271
< 0.1%
37681
< 0.1%
33311
< 0.1%
28781
< 0.1%
20221
< 0.1%
20121
< 0.1%
17761
< 0.1%
15941
< 0.1%

avg_basket_size
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1973
Distinct (%)66.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean236.045703
Minimum1
Maximum6009.333333
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2021-11-19T15:49:58.780963image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile44.04444444
Q1103.3
median172
Q3281.5
95-th percentile598.54
Maximum6009.333333
Range6008.333333
Interquartile range (IQR)178.2

Descriptive statistics

Standard deviation283.9474436
Coefficient of variation (CV)1.202934177
Kurtosis102.8409514
Mean236.045703
Median Absolute Deviation (MAD)82.75
Skewness7.7098636
Sum699875.5094
Variance80626.15071
MonotonicityNot monotonic
2021-11-19T15:49:58.898844image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10011
 
0.4%
11410
 
0.3%
739
 
0.3%
829
 
0.3%
869
 
0.3%
1368
 
0.3%
888
 
0.3%
1638
 
0.3%
1408
 
0.3%
758
 
0.3%
Other values (1963)2877
97.0%
ValueCountFrequency (%)
11
< 0.1%
21
< 0.1%
3.3333333331
< 0.1%
5.3333333331
< 0.1%
5.6666666671
< 0.1%
6.1428571431
< 0.1%
7.51
< 0.1%
91
< 0.1%
9.51
< 0.1%
111
< 0.1%
ValueCountFrequency (%)
6009.3333331
< 0.1%
42821
< 0.1%
39061
< 0.1%
3868.651
< 0.1%
28801
< 0.1%
28011
< 0.1%
2733.9444441
< 0.1%
2518.7692311
< 0.1%
2160.3333331
< 0.1%
2082.2258061
< 0.1%

avg_unique_basket_size
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1973
Distinct (%)66.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean236.045703
Minimum1
Maximum6009.333333
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2021-11-19T15:49:59.033854image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile44.04444444
Q1103.3
median172
Q3281.5
95-th percentile598.54
Maximum6009.333333
Range6008.333333
Interquartile range (IQR)178.2

Descriptive statistics

Standard deviation283.9474436
Coefficient of variation (CV)1.202934177
Kurtosis102.8409514
Mean236.045703
Median Absolute Deviation (MAD)82.75
Skewness7.7098636
Sum699875.5094
Variance80626.15071
MonotonicityNot monotonic
2021-11-19T15:49:59.152858image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10011
 
0.4%
11410
 
0.3%
739
 
0.3%
829
 
0.3%
869
 
0.3%
1368
 
0.3%
888
 
0.3%
1638
 
0.3%
1408
 
0.3%
758
 
0.3%
Other values (1963)2877
97.0%
ValueCountFrequency (%)
11
< 0.1%
21
< 0.1%
3.3333333331
< 0.1%
5.3333333331
< 0.1%
5.6666666671
< 0.1%
6.1428571431
< 0.1%
7.51
< 0.1%
91
< 0.1%
9.51
< 0.1%
111
< 0.1%
ValueCountFrequency (%)
6009.3333331
< 0.1%
42821
< 0.1%
39061
< 0.1%
3868.651
< 0.1%
28801
< 0.1%
28011
< 0.1%
2733.9444441
< 0.1%
2518.7692311
< 0.1%
2160.3333331
< 0.1%
2082.2258061
< 0.1%

Interactions

2021-11-19T15:49:54.089476image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:36.269225image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:37.784225image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:39.271224image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:40.680627image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:42.150873image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:43.536115image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:45.078800image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:46.673796image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:48.120782image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:49.662713image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:51.188083image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:52.563980image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:54.194476image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:36.382226image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:37.894226image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:39.373225image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:40.785624image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:42.255090image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:43.648118image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:45.201820image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:46.776798image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:48.231783image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:49.772706image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:51.291783image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:52.670979image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:54.302475image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:36.490226image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:38.013226image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:39.480223image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:40.896624image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:42.358436image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:43.767115image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:45.329240image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:46.882796image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:48.370783image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:49.885704image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:51.395218image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:52.778980image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:54.410076image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:36.694226image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:38.120225image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:39.584225image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:41.019063image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:42.464405image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:43.879516image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:45.449239image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:46.996136image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:48.506784image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:50.009705image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:51.498217image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:52.957730image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:54.525073image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:36.810226image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:38.240225image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:39.694225image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:41.136024image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:42.570401image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:44.007770image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:45.581758image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:47.105137image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:48.628109image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:50.126131image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:51.602215image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:53.071687image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:54.631074image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:36.914229image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:38.351230image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:39.793226image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:41.242853image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:42.668402image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:44.116773image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:45.695631image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:47.204137image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:48.739087image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:50.239637image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:51.704216image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:53.179085image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:54.750075image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:37.032224image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:38.470225image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:39.907225image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:41.362852image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:42.783012image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:44.245775image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:45.829632image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:47.315136image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:48.857960image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:50.376667image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:51.817216image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:53.297069image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:54.869074image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:37.151225image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:38.585225image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:40.030382image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:41.481940image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:42.897452image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:44.372774image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:45.976776image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:47.431540image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:48.983329image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:50.505948image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:51.932098image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:53.419068image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:54.983078image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:37.250225image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:38.698245image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:40.131861image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:41.581283image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:43.007551image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:44.487155image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:46.088219image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:47.534710image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:49.095330image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:50.606943image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:52.035077image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:53.521072image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:55.096074image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:37.355225image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:38.807225image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:40.244860image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:41.702299image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:43.120320image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:44.602419image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:46.207109image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:47.646518image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:49.213329image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:50.731974image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:52.141979image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:53.637069image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:55.212390image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:37.469225image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:38.918229image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:40.362860image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:41.813686image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:43.230611image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:44.721144image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:46.336656image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:47.756976image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:49.328329image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:50.843950image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:52.249979image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:53.750068image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:55.315016image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:37.569225image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:39.040225image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:40.465861image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:41.921685image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:43.326271image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:44.833613image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:46.444310image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:47.857979image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:49.436706image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:50.955664image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:52.348982image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:53.855068image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:55.427016image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:37.678225image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:39.156225image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:40.574860image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:42.035875image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:43.429979image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:44.953765image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:46.557365image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:47.987240image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:49.547706image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:51.074084image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:52.457979image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:49:53.972068image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2021-11-19T15:49:59.268863image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-11-19T15:49:59.443824image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-11-19T15:49:59.616752image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-11-19T15:49:59.794197image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-11-19T15:49:55.636017image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2021-11-19T15:49:55.855573image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indexcustomer_idgross_revenuerecency_daysqtde_invoicesqtde_itemsqtde_productsavg_ticketavg_recency_daysfrequencyqtde_returnsavg_basket_sizeavg_unique_basket_size
00178505391.2100372.000034.00001733.0000297.000018.152235.500017.000040.000050.970650.9706
11130473232.590056.00009.00001390.0000171.000018.904027.25000.028335.0000154.4444154.4444
22125836705.38002.000015.00005028.0000232.000028.902523.18750.040350.0000335.2000335.2000
3313748948.250095.00005.0000439.000028.000033.866192.66670.01790.000087.800087.8000
4415100876.0000333.00003.000080.00003.0000292.00008.60000.073222.000026.666726.6667
55152914623.300025.000014.00002102.0000102.000045.326523.20000.040129.0000150.1429150.1429
66146885630.87007.000021.00003621.0000327.000017.219818.30000.0572399.0000172.4286172.4286
77178095411.910016.000012.00002057.000061.000088.719835.70000.033541.0000171.4167171.4167
881531160767.90000.000091.000038194.00002379.000025.54354.14440.2433474.0000419.7143419.7143
99160982005.630087.00007.0000613.000067.000029.934847.66670.02440.000087.571487.5714

Last rows

df_indexcustomer_idgross_revenuerecency_daysqtde_invoicesqtde_itemsqtde_productsavg_ticketavg_recency_daysfrequencyqtde_returnsavg_basket_sizeavg_unique_basket_size
29555601177271060.250015.00001.0000645.000066.000016.06446.00001.00006.0000645.0000645.0000
2956561117232421.52002.00002.0000203.000036.000011.708912.00000.15380.0000101.5000101.5000
2957561217468137.000010.00002.0000116.00005.000027.40004.00000.40000.000058.000058.0000
2958562313596697.04005.00002.0000406.0000166.00004.19907.00000.25000.0000203.0000203.0000
29595629148931237.85009.00002.0000799.000073.000016.95682.00000.66670.0000399.5000399.5000
2960563312479473.200011.00001.0000382.000030.000015.77334.00001.000034.0000382.0000382.0000
2961565414126706.13007.00003.0000508.000015.000047.07533.00000.750050.0000169.3333169.3333
29625660135211092.39001.00003.0000733.0000435.00002.51124.50000.30000.0000244.3333244.3333
2963567015060301.84008.00004.0000262.0000120.00002.51531.00002.00000.000065.500065.5000
2964568912558269.96007.00001.0000196.000011.000024.54186.00001.0000196.0000196.0000196.0000